Conversation
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@tmigot please review. On my system they are passing when I use one(T), T(i) and T(mod(i,5)). |
Co-authored-by: Tangi Migot <tangi.migot@gmail.com>
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@tmigot Worked on your review comments but now seeing |
Co-authored-by: Tangi Migot <tangi.migot@gmail.com>
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Pull request overview
This pull request adds three new optimization test problems (Problems 9, 10, and 16) from the Luksan, Matonoha, and Vlcek test problem collection. The implementation follows the established pattern for optimization problems in this repository, providing three variants: PureJuMP models, ADNLPProblems models, and metadata files.
Key Changes:
- Added three trigonometric optimization problems:
trig(Problem 9),toint(Problem 10), andtrigb(Problem 16) - Each problem is implemented in three formats: PureJuMP, ADNLPProblems, and metadata configurations
- All problems are unconstrained minimization problems with variable dimensions
Reviewed changes
Copilot reviewed 9 out of 9 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| src/PureJuMP/trig.jl | PureJuMP implementation of the "Another trigonometric function" problem (Problem 9) |
| src/PureJuMP/toint.jl | PureJuMP implementation of the "Toint trigonometric function" problem (Problem 10) |
| src/PureJuMP/trigb.jl | PureJuMP implementation of the "Banded trigonometric problem" (Problem 16) |
| src/ADNLPProblems/trig.jl | ADNLPProblems implementation of Problem 9 using automatic differentiation |
| src/ADNLPProblems/toint.jl | ADNLPProblems implementation of Problem 10 with critical bugs in division operators |
| src/ADNLPProblems/trigb.jl | ADNLPProblems implementation of Problem 16 |
| src/Meta/trig.jl | Metadata configuration for the trig problem |
| src/Meta/toint.jl | Metadata configuration for the toint problem |
| src/Meta/trigb.jl | Metadata configuration for the trigb problem |
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| export toint | |||
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| function toint(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...) where {T} | |||
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There seems to be an inconsistency between the AD version and the JuMP version:
julia> ju_nlp1 = MathOptNLPModel(OptimizationProblems.PureJuMP.toint());
julia> ad_nlp1 = OptimizationProblems.ADNLPProblems.toint();
julia> obj(ad_nlp1, ad_nlp1.meta.x0) == obj(ju_nlp1, ju_nlp1.meta.x0)
false
The other two seem correct.
2 participants
#116
